# load the needed libraries
library(tidyverse)
library(ggplot2)
library(lubridate)
library(readxl)
library(hrbrthemes)
library(viridis)
library(ggpubr)
library(purrr)
library(plotly)
library(dplyr)
library(RColorBrewer)
library(stargazer)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)
#source('readin_lib.R')
#source('student_perf_lib.R')ELA_Standards<-read_csv("data_604/State_Standards-_Mastery_Star_2425_ELA.csv")%>%
mutate(Division = case_when(
Grade == 9 | Grade == 10 ~ "HS",
Grade == 5 | Grade == 6 | Grade == 7 | Grade == 8 ~ "MS"
))%>%
mutate(Grade = as.factor(Grade))%>%
mutate(`Reporting Category` = as.factor(`Reporting Category`))%>%
mutate(`Domain` = as.factor(`Domain`))%>%
mutate(`Division` = as.factor(`Division`))%>%
mutate(`Screening Window` = as.factor(`Screening Window`))%>%
mutate(`Text Type` = as.factor(`Text Type`))
ELA_Standardsstar_dem_all_private<-read_csv("data_604/student_star_dem_all_private.csv")%>%
mutate(`Grade` = as.factor(`Grade`))%>%
mutate(`Grade` = recode_factor(`Grade`,
"5" = "5",
"6" = "6",
"7" = "7",
"8"= "8",
"9"= "9",
"10"= "10",
.ordered = TRUE))%>%
mutate(School_Year = as.factor(School_Year))%>%
mutate(School_Year_Fac = School_Year)%>%
mutate(School_Year = recode_factor(School_Year,
"2023-2024" = "2023-2024",
"2024-2025" = "2024-2025",
.ordered = TRUE))%>%
mutate(`Assignment Type` = as.factor(`Assignment Type`))%>%
mutate(`Test 1 Benchmark Category` = recode_factor(`Test 1 Benchmark Category`,
"At/Above Benchmark" = "At/Above Benchmark",
"On Watch" = "On Watch",
"Intervention" = "Intervention",
"Urgent Intervention"= "Urgent Intervention",
.ordered = TRUE))%>%
mutate(IEP_Status = as.factor(IEP_Status))%>%
mutate(`504_Status` = as.factor(`504_Status`))%>%
mutate(`Low_Income_Status` = as.factor(Low_Income_Status))%>%
mutate(`Gender` = as.factor(Gender) )%>%
mutate(`Gender_Status` = case_when(
Gender == "Male" ~ 0,
Gender == "Female" ~ 1
))
star_dem_all_privateHistSchoolBenchSum<-read_csv("data_604/StarBenchmarkSummary_School.csv")%>%
mutate(Year = as.factor(Year))%>%
mutate(`Screening Window` = as.factor(`Screening Window`))%>%
mutate(`Benchmark_Status` = recode_factor(`Benchmark_Status`,
"At/Above Benchmark" = "At/Above Benchmark",
"Below Benchmark" = "Below Benchmark",
.ordered = TRUE))%>%
select(Year, `Screening Window`, Subject, `Benchmark_Status`, `% Students`)
HistSchoolBenchSumHistGradeGrowthSum<-read_csv("data_604/StarBenchmarkSummary_SGP_Sum.csv")%>%
mutate(Year = as.factor(Year))%>%
mutate(`Screening Window` = as.factor(`Screening Window`))%>%
mutate(`Subject` = as.factor(`Subject`))%>%
mutate(`Grade_Fac` = as.factor(Grade))%>%
mutate(`Grade` = as.factor(Grade))%>%
mutate(`Grade` = recode_factor(`Grade`,
"Grade 5" = "Grade 5",
"Grade 6" = "Grade 6",
"Grade 7" = "Grade 7",
"Grade 8"= "Grade 8",
"Grade 9"= "Grade 9",
"Grade 10"= "Grade 10",
.ordered = TRUE))%>%
select(Year, `Screening Window`, `Grade`, Subject, `Median SGP`)
HistGradeGrowthSumAre Rising Tide students making progress toward our achievement accountability goal: “80% of Rising Students will test as at or above Benchmark in Reading on the Renaissance Star Reading and Star Mathematics screening assessments?
star_dem_all_private<-star_dem_all_private%>%
mutate(Benchmark_Status =
case_when(`Test 1 PR` >= 40 ~ "At/Above Benchmark",
`Test 1 PR` < 40 ~ "Below Benchmark"))%>%
mutate(Benchmark_Status = as.factor(Benchmark_Status))H1_null_Reading: There is no difference in the percentage of Rising Tide students classified as at or above benchmark in Reading in SY 2023-2024 and SY 2024-2025.
H1_alt_Reading: The percentage of Rising Tide students classified as at or above benchmark in Reading in SY 2023-2024 is less than the percentage of students classified as at or above benchmark in Reading in SY 2024-2025.
H1_null_Math: There is no difference in the percentage of Rising Tide students classified as at or above benchmark in Math in SY 2023-2024 and SY 2024-2025.
H1_alt_Math: The percentage of Rising Tide students classified as at or above benchmark in Math in SY 2023-2024 is less than the percentage of students classified as at or above benchmark in Math in SY 2024-2025.
star_dem_math<- star_dem_all_private%>%
filter(`Assignment Type` == "Star Math")
star_dem_mathfit_Math_achieve = lm(`Test 1 PR` ~ School_Year_Fac + `IEP_Status` + `504_Status` + `Low_Income_Status`, data = star_dem_math)
summary(fit_Math_achieve)
Call:
lm(formula = `Test 1 PR` ~ School_Year_Fac + IEP_Status + `504_Status` +
Low_Income_Status, data = star_dem_math)
Residuals:
Min 1Q Median 3Q Max
-59.329 -17.173 1.028 17.497 67.574
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 64.173 1.245 51.531 < 2e-16 ***
School_Year_Fac2024-2025 4.157 1.472 2.824 0.00484 **
IEP_Status1 -32.747 1.730 -18.928 < 2e-16 ***
`504_Status`1 -1.826 2.296 -0.796 0.42649
Low_Income_Status1 -6.454 1.645 -3.923 9.37e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 22.78 on 956 degrees of freedom
Multiple R-squared: 0.2929, Adjusted R-squared: 0.2899
F-statistic: 99 on 4 and 956 DF, p-value: < 2.2e-16AIC(fit_Math_achieve)[1] 8742.117BIC(fit_Math_achieve)[1] 8771.324#star_dem_math
star_dem_mathstar_dem_ELA<- star_dem_all_private%>%
filter(`Assignment Type` == "Star Reading")
fit_ELA_achieve = lm(`Test 1 PR` ~ School_Year_Fac + `IEP_Status`, data = star_dem_ELA)
summary(fit_ELA_achieve)
Call:
lm(formula = `Test 1 PR` ~ School_Year_Fac + IEP_Status, data = star_dem_ELA)
Residuals:
Min 1Q Median 3Q Max
-58.281 -19.490 0.346 19.346 61.510
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 58.654 1.189 49.35 < 2e-16 ***
School_Year_Fac2024-2025 4.627 1.553 2.98 0.00296 **
IEP_Status1 -26.164 1.783 -14.67 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 24.1 on 961 degrees of freedom
Multiple R-squared: 0.1884, Adjusted R-squared: 0.1868
F-statistic: 111.6 on 2 and 961 DF, p-value: < 2.2e-16AIC(fit_ELA_achieve)[1] 8876.38BIC(fit_ELA_achieve)[1] 8895.864Bench_Sum_Plot<-HistSchoolBenchSum%>%
filter(`Year` == "2024-2025" & `Screening Window` == "Winter" | `Year` == "2023-2024" & `Screening Window` == "Spring")%>%
filter(`Benchmark_Status` == "At/Above Benchmark")%>%
ggplot(aes(x=`Benchmark_Status`,fill = `Year`, y = `% Students`)) +
geom_bar(position="dodge", stat = "identity") +
geom_text(aes( y = `% Students`, label = `% Students`,
vjust = -.25), position = position_dodge(width = 1)) +
#theme_classic() +
theme(axis.title.x=element_blank(),
# axis.text.x = element_text(angle = 45, , hjust = 1),
axis.ticks.x=element_blank())+
scale_fill_brewer(palette = "Paired")+
labs(
y = "% Students",
x= "Benchmark Category",
title = "Rising Tide Benchmark Achievement by School Year",
caption = "Source: Renaissance Star Literacy and Math Screening")+
geom_hline(yintercept = 80, color = "grey")+
#annotate("text", x = .75, y = 85, label = "80% Goal")# +
facet_wrap(~Subject)#| eval: false
#| fig-cap: "Rising Tide Benchmark Achievement by School Year, Renaissance Star Literacy and Math"
#| fig-alt: >
#| A bar blot with school year (ordered, factor) on the x-axis and the percentage of students
#| classified as At/Above benchmark on the y-axis, faceted by subject (English Language Arts or Math)
#| Plot illustrates the relationship between the three variables.
Bench_Sum_Plot
Growth_Sum_ELA<-star_dem_all_private%>%
filter(`School_Year` == "2024-2025")%>%
filter(`Assignment Type` == "Star Reading")%>%
group_by(Grade)%>%
summarize(median_SGP= median(`SGP (Expectation=50)`))
Growth_Sum_ELA_Plot<-ggplot(Growth_Sum_ELA, aes(x=`Grade`, y = `median_SGP`,fill="skyblue")) +
geom_bar(position="dodge", stat = "identity", fill="skyblue") +
#facet_wrap(~Grade)+
geom_text(aes( y = `median_SGP`, label = `median_SGP`,
vjust = -.25))+
theme_classic()+
#scale_color_brewer(palette = "Blues")+
theme(axis.title.x=element_blank(),
#axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
labs(
y = "Median SGP",
x= "Grade Level",
title = "Literacy Student Growth Percentile by Grade Level",
caption = "Source: 2024-2025 Renaissance Star Screening")+
geom_hline(yintercept = 50, color = "grey")+
annotate("text", x = 1., y = 52, label = "50th Percentile")Growth_Sum_ELA_PlotHist_Growth_Sum_ELA_Plot<-HistGradeGrowthSum%>%
filter(Subject == "ELA")%>%
filter(`Screening Window` == "Fall-Winter")%>%
ggplot(aes(x=`Grade`,fill = "skyblue", y = `Median SGP`)) +
geom_bar(position="dodge", stat = "identity", fill = "skyblue") +
#facet_wrap(~Grade)+
geom_text(aes( y = `Median SGP`, label = `Median SGP`,
vjust = -.25), position = position_dodge(width = 1))+
#theme_classic()+
#scale_fill_brewer(palette = "Blues")+
theme(axis.title.x=element_blank(),
#axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
labs(
y = "Median SGP",
x= "Grade Level",
title = "Literacy Student Growth Percentile by Grade Level",
caption = "Source: Renaissance Star Literacy Screening")+
geom_hline(yintercept = 50, color = "grey")+
#annotate("text", x = 1.55, y = 52, label = "50th Percentile")+
facet_wrap(~Year)Hist_Growth_Sum_ELA_Plot
Growth_Sum_Math<-star_dem_all_private%>%
filter(`Assignment Type` == "Star Math")%>%
filter(`School_Year` == "2024-2025")%>%
group_by(Grade)%>%
summarize(median_SGP= median(`SGP (Expectation=50)`))
Growth_Sum_Math_Plot<-ggplot(Growth_Sum_Math, aes(x=`Grade`,fill = `Grade`, y = `median_SGP`, fill="skyblue")) +
geom_bar(position="dodge", stat = "identity", fill="skyblue") +
#facet_wrap(~Grade)+
geom_text(aes( y = `median_SGP`, label = `median_SGP`,
vjust = -.25))+
theme_classic()+
scale_fill_brewer(palette = "Blues")+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
labs(
y = "Median SGP",
x= "Grade Level",
title = "Math Student Growth Percentile by Grade Level",
caption = "Source: 2024-2025 Renaissance Star Math Screening")+
geom_hline(yintercept = 50, color = "grey")+
annotate("text", x = 1.05, y = 53, label = "50th Percentile")Growth_Sum_Math_PlotHist_Growth_Sum_Math_Plot<-HistGradeGrowthSum%>%
filter(Subject == "Math")%>%
filter(`Screening Window` == "Fall-Winter")%>%
ggplot(aes(x=`Grade`,fill = "skyblue", y = `Median SGP`)) +
geom_bar(position="dodge", stat = "identity", fill = "skyblue") +
#facet_wrap(~Grade)+
geom_text(aes( y = `Median SGP`, label = `Median SGP`,
vjust = -.25), position = position_dodge(width = 1))+
#theme_classic()+
#scale_fill_brewer(palette = "Blues")+
theme(axis.title.x=element_blank(),
#axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
labs(
y = "Median SGP",
x= "Grade Level",
title = "Math Student Growth Percentile by Grade Level",
caption = "Source: Renaissance Star Math Screening")+
geom_hline(yintercept = 50, color = "grey")+
annotate("text", x = 5.5, y = 48, label = "50th Percentile")+
facet_wrap(~Year)Hist_Growth_Sum_Math_Plot
Is there a relationship between Rising Tide’s Grade 5 Mathematics curriculum initiatives and Grade 5 student growth in mathematics?
H2_null: There is no difference in the student growth percentile (SGP) for Grade 5 students in SY 2023-2024 and SY 2024-2025
H2_alt: The student growth percentile (SGP) for Grade 5 students in SY 2023-2024 is less than the student growth percentile (SGP) for Grade 5 students in SY 2024-2025.
Math_IEP_Sum<-star_dem_math%>%
mutate(IEP = case_when(IEP_Status == 1 ~ "Yes",
IEP_Status == 0 ~ "No"))%>%
filter(`Assignment Type` == "Star Math")%>%
group_by(Grade, `School_Year`, IEP)%>%
summarize(`Median SGP` = median(`SGP (Expectation=50)`))MathHistGrowth<- star_dem_math
G5MathHistGrowth<-star_dem_math%>%
filter(Grade == "5")
G5_Hist_Year <- G5MathHistGrowth %>%
ggplot( aes(x=`SGP (Expectation=50)`, fill=School_Year)) +
geom_histogram( color="#e9ecef", alpha=0.6,binwidth=10, position = 'identity') +
scale_fill_manual(values=c("#69b3a2", "#404080")) +
theme_classic() +
labs(fill="")#+
# facet_wrap(~Year)
G5_Hist_Year
G5_Hist_Year_Facet <- G5MathHistGrowth %>%
ggplot( aes(x=`SGP (Expectation=50)`, fill=School_Year)) +
geom_histogram( color="#e9ecef", alpha=0.6,binwidth=15, position = 'identity') +
scale_fill_manual(values=c("#69b3a2", "#404080")) +
theme_classic() +
labs(fill="")+
labs( y = "",
title = "Grade 5 Student Growth Distribution",
x = "Student Growth Percentile (SGP)",
caption = "Star Math Assessments")+
facet_wrap(~School_Year)
G5_Hist_Year_Facet
Growth_Hist<-MathHistGrowth%>%
ggplot( aes(x= `SGP (Expectation=50)`)) +
#geom_histogram(alpha=0.6, binwidth = 15) +
#ggplot(aes()+
geom_histogram(alpha = .9, binwidth = 15)+
#scale_fill_viridis(discrete=TRUE) +
#scale_color_viridis(discrete=TRUE) +
theme_classic()
#Growth_Hist#summary(MathHistGrowth)
G5MathHistGrowth<-G5MathHistGrowth%>%
mutate(School_Year_Factor = case_when(
School_Year == "2023-2024" ~ "2023-2024",
School_Year == "2024-2025" ~ "2024-2025"))%>%
mutate(School_Year_Factor = as.factor(School_Year_Factor))%>%
mutate(School_Year_Bool = case_when(
`School_Year` == "2023-2024" ~ 0,
`School_Year` == "2024-2025" ~ 1
))
fit_G5Math_growth_dem = lm(`SGP (Expectation=50)` ~ School_Year_Factor + `IEP_Status` + `Low_Income_Status` + `504_Status` + `Test 1 PR`, data = G5MathHistGrowth)
summary(fit_G5Math_growth_dem)
Call:
lm(formula = `SGP (Expectation=50)` ~ School_Year_Factor + IEP_Status +
Low_Income_Status + `504_Status` + `Test 1 PR`, data = G5MathHistGrowth)
Residuals:
Min 1Q Median 3Q Max
-56.13 -25.43 -0.01 27.66 53.29
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 51.06482 7.30772 6.988 5.97e-11 ***
School_Year_Factor2024-2025 13.82626 4.57343 3.023 0.00289 **
IEP_Status1 -17.00046 6.14880 -2.765 0.00632 **
Low_Income_Status1 -3.28838 4.97102 -0.662 0.50918
`504_Status`1 -0.77230 6.88859 -0.112 0.91086
`Test 1 PR` -0.08415 0.09742 -0.864 0.38889
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 29.99 on 171 degrees of freedom
Multiple R-squared: 0.09237, Adjusted R-squared: 0.06583
F-statistic: 3.48 on 5 and 171 DF, p-value: 0.005071AIC(fit_G5Math_growth_dem)[1] 1714.086BIC(fit_G5Math_growth_dem)[1] 1736.319stargazer(fit_G5Math_growth_dem, type = "text")
=======================================================
Dependent variable:
---------------------------
`SGP (Expectation=50)`
-------------------------------------------------------
School_Year_Factor2024-2025 13.826***
(4.573)
IEP_Status1 -17.000***
(6.149)
Low_Income_Status1 -3.288
(4.971)
`504_Status`1 -0.772
(6.889)
`Test 1 PR` -0.084
(0.097)
Constant 51.065***
(7.308)
-------------------------------------------------------
Observations 177
R2 0.092
Adjusted R2 0.066
Residual Std. Error 29.988 (df = 171)
F Statistic 3.480*** (df = 5; 171)
=======================================================
Note: *p<0.1; **p<0.05; ***p<0.01plot(fit_G5Math_growth_dem, which = 1:6)
cooks= 4/179
cooks #.0223[1] 0.02234637Hist_Growth_Sum_G5Math_Plot<-HistGradeGrowthSum%>%
filter(Subject == "Math")%>%
filter(`Screening Window` == "Fall-Winter")%>%
ggplot(aes(x=`Grade`,fill = ifelse(Grade == "5", "A", "B"), y = `Median SGP`)) +
geom_bar(position="dodge", stat = "identity", show.legend=FALSE) +
scale_fill_manual(values = c(A= "skyblue", B = "grey"))+
#facet_wrap(~Grade)+
geom_text(aes( y = `Median SGP`, label = `Median SGP`,
vjust = -.25), position = position_dodge(width = 1))+
#theme_classic()+
#scale_fill_brewer(palette = "Blues")+
theme(axis.title.x=element_blank(),
#axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
labs(
y = "Median SGP",
x= "Grade Level",
title = "Math Student Growth Percentile by Grade Level",
caption = "Source: Renaissance Star Math Screening")+
geom_hline(yintercept = 50, color = "grey")+
#annotate("text", x = 5.5, y = 48, label = "50th Percentile")+
facet_wrap(~Year)Hist_Growth_Sum_G5Math_Plot
Hist_Growth_G5Math_Plot<-HistGradeGrowthSum%>%
filter(Subject == "Math")%>%
filter(Grade == "5")%>%
filter(`Screening Window` == "Fall-Winter")%>%
ggplot(aes(x=`Year`,fill = "skyblue", y = `Median SGP`)) +
geom_bar(position="dodge", stat = "identity", fill = "skyblue") +
#facet_wrap(~Grade)+
geom_text(aes( y = `Median SGP`, label = `Median SGP`,
vjust = -.25), position = position_dodge(width = 1))+
theme_classic()+
scale_fill_brewer(palette = "Paired")+
theme(axis.title.x=element_blank(),
#axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
labs(
y = "Median SGP",
x= "School Year",
title = "Improvement in Grade 5 Student Growth Percentile",
caption = "Source: Renaissance Star Math Screening")+
geom_hline(yintercept = 50, color = "grey")+
annotate("text", x = 1., y = 52, label = "Goal 50th Percentile")#+
#theme_classic()
#facet_wrap(~Year)Hist_Growth_G5Math_Plot
IEP_Growth_G5Math_Plot<- Math_IEP_Sum%>%
filter(Grade == "5")%>%
ggplot(aes(x=`IEP`,fill = `School_Year`, y = `Median SGP`)) +
geom_bar(position="dodge", stat = "identity") +
geom_text(aes( y = `Median SGP`, x = `IEP`, label = `Median SGP`), position = position_dodge(width=.9),
vjust = 1)+
theme_classic()+
theme(#axis.title.x=element_blank(),
#axis.text.x=element_blank(),
# axis.text.x=element_text(angle =45, hjust = 1),
axis.ticks.x=element_blank())+
scale_fill_brewer(palette = "Paired")+
# facet_wrap(~`Screening Window`)+
labs(
y = "Median SGP",
x= "IEP Status",
title = "Improved Growth For All Grade 5 Learners",
caption = "Source: Renaissance Star Math Screening")+
geom_hline(yintercept = 50, color = "grey")+
annotate("text", x =.70, y = 52, label = "50th Percentile")#+IEP_Growth_G5Math_Plot
Is there a relationship between the text type of a literacy standard (informational vs. literature) and Rising Tide High School students’ mastery of the standard?
H3_null: There is no difference between Rising Tide High School students’ mean mastery score for literature reading standards and their mean mastery score for informational text reading standards.
H3_alt: Rising Tide High School students’ mean mastery score for literature reading standards is higher than their mean mastery score for informational text reading standards.
HS_ELA_Standards<-ELA_Standards%>%
filter(Grade == 9|Grade == 10)%>%
filter(`Text Type` == "Informational Text" | `Text Type` == "Literature")
HS_ELA_StandardsHS_ELA_Text_Facet <- HS_ELA_Standards %>%
ggplot( aes(x=`% Secure`, fill=`Text Type`)) +
geom_histogram( color="#e9ecef", alpha=0.6,binwidth=15, position = 'identity') +
scale_fill_manual(values=c("#69b3a2", "#404080")) +
theme_classic() +
labs( y = "",
title = "High School State Standard Mastery Distribution",
x = "% Students Secure",
caption = "Star Reading: State Standards Mastery")+
facet_wrap(~`Text Type`)
HS_ELA_Text_Facet
fit_ELA_text = lm(`% Secure` ~ ( `Text Type` + `Screening Window` + `Domain`), data = ELA_Standards)
summary(fit_ELA_text)
Call:
lm(formula = `% Secure` ~ (`Text Type` + `Screening Window` +
Domain), data = ELA_Standards)
Residuals:
Min 1Q Median 3Q Max
-40.934 -8.667 2.097 8.740 34.360
Coefficients:
Estimate Std. Error t value
(Intercept) 47.593 1.932 24.631
`Text Type`Literature 4.294 1.707 2.515
`Screening Window`Winter 6.658 1.698 3.920
DomainIntegration of Knowledge and Ideas 4.310 2.248 1.917
DomainKey Ideas and Details 2.458 2.137 1.150
DomainRange of Reading and Level of Text Complexity -4.611 3.022 -1.526
Pr(>|t|)
(Intercept) < 2e-16 ***
`Text Type`Literature 0.012609 *
`Screening Window`Winter 0.000118 ***
DomainIntegration of Knowledge and Ideas 0.056484 .
DomainKey Ideas and Details 0.251263
DomainRange of Reading and Level of Text Complexity 0.128519
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 12.82 on 222 degrees of freedom
(309 observations deleted due to missingness)
Multiple R-squared: 0.121, Adjusted R-squared: 0.1012
F-statistic: 6.11 on 5 and 222 DF, p-value: 2.523e-05fit_HS_ELA_text_domain = lm(`% Secure` ~ ( `Text Type` + `Screening Window` + `Domain`), data = HS_ELA_Standards)
summary(fit_HS_ELA_text_domain)
Call:
lm(formula = `% Secure` ~ (`Text Type` + `Screening Window` +
Domain), data = HS_ELA_Standards)
Residuals:
Min 1Q Median 3Q Max
-28.378 -8.155 -1.182 7.971 40.030
Coefficients:
Estimate Std. Error t value
(Intercept) 39.314 3.175 12.382
`Text Type`Literature 8.840 2.806 3.151
`Screening Window`Winter 4.947 2.791 1.773
DomainIntegration of Knowledge and Ideas 7.276 3.694 1.970
DomainKey Ideas and Details 1.708 3.512 0.486
DomainRange of Reading and Level of Text Complexity 3.417 4.967 0.688
Pr(>|t|)
(Intercept) <2e-16 ***
`Text Type`Literature 0.0024 **
`Screening Window`Winter 0.0806 .
DomainIntegration of Knowledge and Ideas 0.0528 .
DomainKey Ideas and Details 0.6282
DomainRange of Reading and Level of Text Complexity 0.4938
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 12.17 on 70 degrees of freedom
(38 observations deleted due to missingness)
Multiple R-squared: 0.1886, Adjusted R-squared: 0.1307
F-statistic: 3.255 on 5 and 70 DF, p-value: 0.01063AIC(fit_HS_ELA_text_domain)[1] 603.2177BIC(fit_HS_ELA_text_domain)[1] 619.5328stargazer(fit_ELA_text, type = "text")
===============================================================================
Dependent variable:
---------------------------
`% Secure`
-------------------------------------------------------------------------------
`Text Type`Literature 4.294**
(1.707)
`Screening Window`Winter 6.658***
(1.698)
DomainIntegration of Knowledge and Ideas 4.310*
(2.248)
DomainKey Ideas and Details 2.458
(2.137)
DomainRange of Reading and Level of Text Complexity -4.611
(3.022)
Constant 47.593***
(1.932)
-------------------------------------------------------------------------------
Observations 228
R2 0.121
Adjusted R2 0.101
Residual Std. Error 12.823 (df = 222)
F Statistic 6.110*** (df = 5; 222)
===============================================================================
Note: *p<0.1; **p<0.05; ***p<0.01stargazer(fit_HS_ELA_text_domain, type = "text")
===============================================================================
Dependent variable:
---------------------------
`% Secure`
-------------------------------------------------------------------------------
`Text Type`Literature 8.840***
(2.806)
`Screening Window`Winter 4.947*
(2.791)
DomainIntegration of Knowledge and Ideas 7.276*
(3.694)
DomainKey Ideas and Details 1.708
(3.512)
DomainRange of Reading and Level of Text Complexity 3.417
(4.967)
Constant 39.314***
(3.175)
-------------------------------------------------------------------------------
Observations 76
R2 0.189
Adjusted R2 0.131
Residual Std. Error 12.166 (df = 70)
F Statistic 3.255** (df = 5; 70)
===============================================================================
Note: *p<0.1; **p<0.05; ***p<0.01#ELA_Standards
ELA_Text_Summary<-ELA_Standards%>%
filter(`Reporting Category` == "Reading")%>%
group_by(`Text Type`, `Screening Window`)%>%
summarize(`Mean % Secure` = round(mean(`% Secure`)),
`Median % Secure` = round(median(`% Secure`)))
#ELA_Text_Summary
ELA_Text_Summary%>%
filter(`Screening Window` != "Spring")%>%
ggplot(aes(x=`Text Type`,fill = `Text Type`, y = `Mean % Secure`)) +
geom_bar(position="dodge", stat = "identity") +
geom_text(aes( y = `Mean % Secure`, x = `Text Type`, label = `Mean % Secure`), position = position_dodge(width=.9),
vjust = 1)+
theme(axis.title.x=element_blank(),
#axis.text.x=element_blank(),
axis.text.x=element_text(angle =45, hjust = 1),
axis.ticks.x=element_blank())+
scale_fill_brewer(palette = "Paired")+
facet_wrap(~`Screening Window`)+
labs(
y = "Mean % Secure",
x= "Domainl",
title = "School State Standard Mastery by Text Type ELA",
caption = "Source: 2024-2025 Renaissance Star Reading Screening")
ELA_Text_Domain_Summary<-ELA_Standards%>%
filter(`Reporting Category` == "Reading")%>%
group_by(`Text Type`, `Domain`, `Screening Window`)%>%
summarize(`Mean % Secure` = round(mean(`% Secure`)),
`Median % Secure` = round(median(`% Secure`)))
#ELA_Text_Domain_Summary
ELA_Text_Div_Summary<-ELA_Standards%>%
filter(`Reporting Category` == "Reading")%>%
group_by(`Text Type`, `Division`, `Screening Window`)%>%
summarize(`Mean % Secure` = round(mean(`% Secure`)),
`Median % Secure` = round(median(`% Secure`)))
#ELA_Text_Div_SummaryELA_Text_Div_Summary%>%
filter(`Screening Window` != "Spring")%>%
ggplot(aes(x=`Text Type`,fill = `Screening Window`, y = `Mean % Secure`)) +
geom_bar(position="dodge", stat = "identity") +
geom_text(aes( y = `Mean % Secure`, x = `Text Type`, label = `Mean % Secure`), position = position_dodge(width=.9),
vjust = 1)+
theme(axis.title.x=element_blank(),
#axis.text.x=element_blank(),
axis.text.x=element_text(angle =45, hjust = 1),
axis.ticks.x=element_blank())+
scale_fill_brewer(palette = "Paired")+
facet_wrap(~`Division`)+
labs(
y = "Mean % Secure",
x= "Domainl",
title = "School State Standard Mastery by Text Type ELA",
caption = "Source: 2024-2025 Renaissance Star Reading Screening")
ELA_Text_Domain_Summary%>%
filter(`Screening Window` != "Spring")%>%
ggplot(aes(x=`Text Type`,fill = `Screening Window`, y = `Mean % Secure`)) +
geom_bar(position="dodge", stat = "identity") +
geom_text(aes( y = `Mean % Secure`, x = `Text Type`, label = `Mean % Secure`), position = position_dodge(width=.9),
vjust = 1)+
theme(axis.title.x=element_blank(),
#axis.text.x=element_blank(),
axis.text.x=element_text(angle =45, hjust = 1),
axis.ticks.x=element_blank())+
scale_fill_brewer(palette = "Paired")+
facet_wrap(~`Domain`)+
labs(
y = "Mean % Secure",
x= "Domain",
title = "School State Standard Mastery by Text Type ELA",
caption = "Source: 2024-2025 Renaissance Star Reading Screening")
ELA_StandardsELA_TextType_Sum_HS<-ELA_Standards%>%
filter(`Division` == "HS")%>%
filter (`Screening Window` != "Spring")%>%
filter(`Reporting Category` == "Reading")%>%
group_by(`Screening Window`, Grade, `Text Type`)%>%
summarize(`Mean % Secure` = round(mean(`% Secure`)))
ELA_TextType_Sum_HS%>%
ggplot(aes(x=`Text Type`,fill = `Screening Window`, y = `Mean % Secure`)) +
geom_bar(position="dodge", stat = "identity") +
#scale_x_reverse()+
geom_text(aes( y = `Mean % Secure`, label = `Mean % Secure`,
vjust = -.25), position = position_dodge(width = 1)) +
#theme_classic() +
theme(axis.title.x=element_blank(),
# axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
scale_fill_brewer(palette = "Paired")+
labs(
y = "Mean % Secure",
x= "Text Type",
title = "HS Reading Standards Mastery by Text Type",
caption = "Source: 2024-2025 Renaissance Star Reading Screening")+
facet_wrap(~Grade)
ELA_TextType_Sum_MS<-ELA_Standards%>%
filter(`Division` == "MS")%>%
filter (`Screening Window` != "Spring")%>%
filter(`Reporting Category` == "Reading")%>%
group_by(`Screening Window`, Grade, `Text Type`)%>%
summarize(`Mean % Secure` = round(mean(`% Secure`)))
ELA_TextType_Sum_MS%>%
ggplot(aes(x=`Text Type`,fill = `Screening Window`, y = `Mean % Secure`)) +
geom_bar(position="dodge", stat = "identity") +
#scale_x_reverse()+
geom_text(aes( y = `Mean % Secure`, label = `Mean % Secure`,
vjust = -.25), position = position_dodge(width = 1)) +
#theme_classic() +
theme(axis.title.x=element_blank(),
# axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
scale_fill_brewer(palette = "Paired")+
labs(
y = "Mean % Secure",
x= "Text Type",
title = "MS Reading Standards Mastery by Text Type",
caption = "Source: 2024-2025 Renaissance Star Reading Screening")+
facet_wrap(~Grade)
#ELA_Standards